SEARCH · Engineering Papers

Results for “Optimization of reliability management”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

NASA NTRS2005

Large Engine Technology Program. Task 22: Variable Geometry Concepts for Rich-Quench-Lean Combustors

The objective of the task reported herein was to define, evaluate, and optimize variable geometry concepts suitable for use with a Rich-Quench-Lean (RQL) combustor. The specific intent was to identify approaches that would satisfy High Speed Civil Transport (HSCT) cycle operational requirements with regard to fuel-air ratio turndown capability, ignition, and stability margin without compromising the stringent emissions, performance, and reliability goals that this combustor would have to achieve. Four potential configurations were identified and three of these were refined and tested in a high-pressure modular RQL combustor rig. The tools used in the evolution of these concepts included models built with rapid fabrication techniques that were tested for airflow characteristics to confirm sizing and airflow management capability, spray patternation, and atomization characterization tests of these models and studies that were supported by Computational Fluid Dynamics analyses. Combustion tests were performed with each of the concepts at supersonic cruise conditions and at other critical conditions in the flight envelope, including the transition points of the variable geometry system, to identify performance, emissions, and operability impacts. Based upon the cold flow characterization, emissions results, acoustic behavior observed during the tests and consideration of mechanical, reliability, and implementation issues, the tri-swirler configuration was selected as the best variable geometry concept for incorporation in the RQL combustor evolution efforts for the HSCT.

Tacina, Robert R.↗

NASA NTRS2024

Quantifying Caloric Expenditure During Zero-G Exercise

BACKGROUND: Exercise is a fundamental component of maintaining astronaut health on long-duration space missions, where the microgravity environment poses unique challenges to physiological homeostasis. Accurate quantification of energy expenditure during such exercises is crucial for optimizing nutritional and physical health strategies for spacefarers. OBJECTIVE: This study aims to develop a comprehensive model to estimate caloric expenditure during exercise in a microgravity environment, employing a combination of spirometry, heart rate data, and other relevant parameters. By assessing energy utilization under these conditions, we seek to facilitate enhanced health management protocols for astronauts in space. METHODS & OUTCOMES: A multivariate predictive model will be constructed, utilizing spirometry and heart rate data, coupled with additional physiological and environmental parameters. A systematic approach will be applied to analyze the relationship between these variables and energy expenditure during various exercises. The proposed model will subsequently undergo rigorous validation to ensure accuracy and reliability. This research is expected to yield a precise and reliable predictive model, contributing to improved strategies for exercise prescription and nutritional intake, addressing the unique challenges presented by microgravity environments. We anticipate that our findings will support the development of more effective health maintenance protocols for astronauts during extended space missions, mitigating the adverse effects of space travel on the human body. SIGNIFICANCE: The development of an accurate and adaptable model to quantify caloric expenditure during exercise in space represents a pivotal advancement in space medicine. The insights gained from this study have the potential to inform the design of enhanced health and wellness strategies, ensuring the well-being and operational effectiveness of astronauts in long-duration space missions.

Calorie↗

DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Unlocking the potential of biogas systems for energy production and climate solutions in rural communities

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others’ energy demands has considerable decarbonization potential.

09 BIOMASS FUELS↗

NASA NTRS2012

A Perspective on NASA Ames Air Traffic Management Research

This paper describes past and present air-traffic-management research at NASA Ames Research Center. The descriptions emerge from the perspective of a technical manager who supervised the majority of this research for the last four years. Past research contributions built a foundation for calculating accurate flight trajectories to enable efficient airspace management in time. That foundation led to two predominant research activities that continue to this day - one in automatically separating aircraft and the other in optimizing traffic flows. Today s national airspace uses many of the applications resulting from research at Ames. These applications include the nationwide deployment of the Traffic Management Advisor, new procedures enabling continuous descent arrivals, cooperation with industry to permit more direct flights to downstream way-points, a surface management system in use by two cargo carriers, and software to evaluate how well flights conform to national traffic management initiatives. The paper concludes with suggestions for prioritized research in the upcoming years. These priorities include: enabling more first-look operational evaluations, improving conflict detection and resolution for climbing or descending aircraft, and focusing additional attention on the underpinning safety critical items such as a reliable datalink.

Schroeder, Jeffery A.↗

NASA NTRS2013

Development of a High Reliability Compact Air Independent PEMFC Power System

Autonomous Underwater Vehicles (AUV's) have received increasing attention in recent years as military and commercial users look for means to maintain a mobile and persistent presence in the undersea world. Compact, neutrally buoyant power systems are needed for both small and large vehicles. Historically, batteries have been employed in these applications, but the energy density and therefore mission duration are limited with current battery technologies. Vehicles with stored energy requirements greater than approximately 10 kWh have an alternate means to get long duration power. High efficiency Proton Exchange Membrane (PEM) fuel cell systems utilizing pure hydrogen and oxygen reactants show the potential for an order of magnitude energy density improvement over batteries as long as the subsystems are compact. One key aspect to achieving a compact and energy dense system is the design of the fuel cell balance of plant (BOP). Recent fuel cell work, initially focused on NASA applications requiring high reliability, has developed systems that can meet target power and energy densities. Passive flow through systems using ejector driven reactant (EDR) circulation have been developed to provide high reactant flow and water management within the stack, with minimal parasitic losses compared to blowers. The ejectors and recirculation loops, along with valves and other BOP instrumentation, have been incorporated within the stack end plate. In addition, components for water management and reactant conditioning have been incorporated within the stack to further minimize the BOP. These BOP systems are thermally and functionally integrated into the stack hardware and fit into the small volumes required for AUV and future NASA applications to maximize the volume available for reactants. These integrated systems provide a compact solution for the fuel cell BOP and maximize the efficiency and reliability of the system. Designs have been developed for multiple applications ranging from less than 1 kWe to 70 kWe. These systems occupy a very small portion of the overall energy system, allowing most of the system volume to be used for reactants. The fuel cell systems have been optimized to use reactants efficiently with high stack efficiency and low parasitic losses. The resulting compact, highly efficient fuel cell system provides exceptional reactant utilization and energy density. Key design variables and supporting test data are presented. Future development activities are described.

Vasquez, Arturo↗

DOE OSTI2022

Avista’s Shared Energy Economy Model Pilot: A Techno-economic Assessment

As part of the second round of the Washington Clean Energy Fund, Avista Corp received a $3.5 million matching grant in support of a shared energy economy project to test the integration of energy assets–from rooftop solar and battery storage to building energy management systems–that can be shared and used for multiple purposes. The goal of this project is to demonstrate how both the customer and the utility can benefit from this shared energy economy model and demonstrate that the electric grid can become more reliable, efficient, resilient, and flexible. Pacific Northwest National Laboratory was engaged by the U.S. Department of Energy and the Washington State Department of Commerce to work with Avista in assessing the benefits of the shared energy economy model. This report documents the techno-economic assessment of the shared energy economy model, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, and case studies and analysis results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Transitioning a Flexible and Scalable Satellite Ground Station Observation Network (GSON) Framework to an Operational Environment

Obtaining accurate and timely satellite observations is of paramount importance in fields like disaster management, weather diagnoses/forecasting, and Earth Sciences remote sensing. Stored mission data (SMD), from low Earth orbiting (LEO) satellite sensors, provides important observations for these fields and applications, however data access to SMD can be delayed from one and half hours to three hours from the time the observations were made. This data latency poses a significant impact on data product optimal use. We developed a Ground Station Observation Network (GSON) that utilizes commercial ground station as a service (GSaaS) providers to acquire low latency direct broadcast (DB) data from AQUA, SNPP, and JPSS-1 satellites using antennas located in strategic locations around the world. We will discuss techniques to improve the deployment efficiency and code reliability and quality of the GSON framework. Topics include right-sizing and containerization of the code to facilitate integration and adaptation with continuous delivery (CD) pipeline, locating non-code assets in referenceable repositories separated from code, adaptation of pipelines as code and simplification of CD, intersecting with code quality tests and checks as part of the pipeline execution and deployment, and establishing distributed repositories, registries, and system identities in a way that mitigates compromise to the CD pipeline. These techniques enable deployment of processing systems that are both highly specific but also dynamically modifiable. This new class of system allows for a flexible and scalable deployment while avoiding the “black box” issues that can plague large system deployments.

cluster↗

DOE OSTI2024

Solar, Wind, and Load Forecasting Dataset for MISO, NYISO, and SPP Balancing Areas

The Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program is an initiative intended to foster "a fundamental shift in grid management rooted in an understanding of asset risk and system risk" (ARPA-E 2020). Launched by the Advanced Research Projects Agency-Energy (ARPA-E), the program supports efforts to incorporate uncertainty in electric power decision making. In support of PERFORM, the National Renewable Energy Laboratory (NREL) has produced a set of time-coincident forecasts of solar, wind, and load profiles. As part of Phase I of the PERFORM effort, NREL created a dataset that consists of one year of time-coincident load, wind, and solar actuals and probabilistic forecasts based on data from the Electric Reliability Council of Texas (ERCOT) (Bryce et al. 2023). In Phase II, NREL developed similar datasets for three other U.S. Independent System Operators (ISO): the Midcontinent Independent System Operator (MISO), the New York Independent System Operator (NYISO), and the Southwest Power Pool (SPP).

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1987

Regenerative fuel cell study for satellites in GEO orbit

Summarized are the results of a 12-month study to identify high performance regenerative hydrogen-oxygen fuel cell concepts for geosynchronous satellite application. Emphasis was placed on concepts with the potential for high energy density (W-hr/lb) and passive means for water and heat management to maximize system reliability. Both polymer membrane and alkaline electrolyte fuel cells were considered, with emphasis on the alkaline cell because of its high performance, advanced state of development, and proven ability to operate in a launch and space environment. Three alkaline system concepts were studied. The first, the integrated design, utilized a configuration in which the fuel cell and electrolysis cells are alternately stacked inside a pressure vessel. Product water is transferred by diffusion during electrolysis and waste heat is conducted through the pressure wall, thus using completely passive means for transfer and control. The second alkaline system, the dedicated design, uses a separate fuel cell and electrolysis stack so that each unit can be optimized in size and weight based on its orbital operating period. The third design was a dual function stack configuration, in which each cell can operate in both fuel cell and electrolysis mode, thus eliminating the need for two separate stacks and associated equipment. Results indicate that using near term technology energy densities between 46 and 52 W-hr/lb can be achieved at efficiencies of 55 percent. System densities of 115 W-hr/lb are contemplated.

Levy, Alexander↗

DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗

DOE OSTI2024

Power over fiber development for HEP detectors

Power-over-Fiber (PoF) technology has been used extensively in settings where high voltages require isolation from ground and electromagnetic isolation is critical. In cryogenic environments, PoF offers a reliable power transmission technology, leveraging optical fibers to transfer power with minimal system degradation. PoF technology excels in maintaining low noise levels and isolation when delivering power to sensitive electronic systems operating in extreme temperature ranges and high voltage environments. Here, in a novel application of PoF for a HEP detector, power is provided to photon detector modules located on a surface at ~300 kV with respect to ground in the planned DUNE experiment. This summary paper of the PoF talk at the 16th PISA Meeting on Advanced Detectors highlights the R&D effort of PoF in extreme conditions and underscores its capacity to revolutionize power delivery and management in critical applications offering a dependable solution with low noise, optimal efficiency, and superior isolation. The DUNE (Abi et al., 2020) experiment will soon deploy large liquid argon (LAr) time projection chambers (TPC) to detect neutrino interactions and other particle physics phenomena. In addition to the particle tracking provided by the TPC, photon detectors, powered by a first ever PoF system, in the cryostat will leverage the high scintillation light yield of LAr to provide crucial timing and additional calorimetric information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2005

Advanced Method to Estimate Fuel Slosh Simulation Parameters

The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. The nutation of a spacecraft spinning about its minor axis typically grows exponentially and the rate of growth is characterized by the Nutation Time Constant (NTC). For launch vehicles using spin-stabilized upper stages, fuel slosh in the spacecraft propellant tanks is usually the primary source of energy dissipation. For analytical prediction of the NTC this fuel slosh is commonly modeled using simple mechanical analogies such as pendulums or rigid rotors coupled to the spacecraft. Identifying model parameter values which adequately represent the sloshing dynamics is the most important step in obtaining an accurate NTC estimate. Analytic determination of the slosh model parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices and elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the equations of motion for the mechanical analog are hand-derived, evaluated, and their results are compared with the experimental results. The proposed research is an effort to automate the process of identifying the parameters of the slosh model using a MATLAB/SimMechanics-based computer simulation of the experimental setup. Different parameter estimation and optimization approaches are evaluated and compared in order to arrive at a reliable and effective parameter identification process. To evaluate each parameter identification approach, a simple one-degree-of-freedom pendulum experiment is constructed and motion is induced using an electric motor. By applying the estimation approach to a simple, accurately modeled system, its effectiveness and accuracy can be evaluated. The same experimental setup can then be used with fluid-filled tanks to further evaluate the effectiveness of the process. Ultimately, the proven process can be applied to the full-sized spinning experimental setup to quickly and accurately determine the slosh model parameters for a particular spacecraft mission. Automating the parameter identification process will save time, allow more changes to be made to proposed designs, and lower the cost in the initial design stages.

Schlee, Keith↗

DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗

NASA NTRS1977

Conceptual definition of Automated Power Systems Management

Automated Power Systems Management (APSM) is defined as the capability of a spacecraft power system to automatically perform monitoring, computational, command, and control functions without ground intervention. Power systems for future planetary spacecraft must have this capability because they must perform up to 10 years, and accommodate real-time changes in mission execution autonomously. Specific APSM functions include fault detection, isolation, and correction; system performance and load profile prediction; power system optimization; system checkout; and data storage and transmission control. This paper describes the basic method of implementing these specific functions. The APSM hardware includes a central power system computer and a processor dedicated to each major power system subassembly along with digital interface circuitry. The major payoffs anticipated are in enhancement of spacecraft reliability and life and reduction of overall spacecraft program cost.

Imamura, M. S.↗

NASA NTRS2026

Ultrasonic Washer–Dryer System for Space Habitats: Design Upgrades and Parabolic Flight Readiness

Clothing makes up nearly 25% of all non-food supplies sent to the International Space Station (ISS). To support sustainable human missions in deep space, NASA’s Life Support and Habitation Systems Focus Area looks to advance technologies to support and improve logistics. Our team is creating a compact ultrasonic clothing washer/dryer system that bypasses traditional limitations and is suitable for space. Thermal drying uses a lot of energy to evaporate water, while our ultrasonic drying method offers a quicker, more efficient alternative. It uses piezoelectric transducers to vibrate textiles at the micron scale, mechanically removing water as cold mist rather than evaporating it. This speeds up drying and reduces energy use, no matter the fabric makeup. This paper details recent upgrades to a full-scale ultrasonic washer–dryer system, readying it for parabolic flight testing. Improvements include an enhanced human–machine interface, better packaging, and optimized performance and control. We also present extensive pre-flight ground tests conducted to ensure reliability and identify potential risks. Collaborating with P&G, we report preliminary cleaning tests using various detergents. These results lay a crucial foundation for the laundry system designed specifically for space. By cutting clothing-related payload and waste by over 97%, this technology supports long-term human exploration missions on the ISS, the Moon, Mars, and beyond.

Ultrasonic↗

NASA NTRS2026

Ultrasonic Washer–Dryer System for Space Habitats: Design Upgrades and Parabolic Flight Readiness

Laundry↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗